Machine learning-based assessment of the built environment on prevalence and severity risks of acne

Understanding the determinants of acne prevalence and severity is crucial for effective prevention and management of this dermatological condition. While urban interventions have long-lasting, far-reaching, and costly implications for health promotion, the associations between built environments (BEs) and acne need further investigation. To address this gap, our study utilizes a nationwide cross-sectional sample of 23,488 undergraduates from 90 campuses in China to conduct a comprehensive analysis. We examined the combined and specific contributions of BEs in relation to other domains of acne-related factors in acne development. By employing the optimal random forest model, our findings reveal that BEs collectively ranked as the second-largest contributors to both the overall prevalence of acne among all participants and the severity of acne in the affected individuals. Moreover, our analysis identifies curvilinear associations between acne and most BEs, underscoring the importance of incorporating BE considerations into the prevention, diagnosis, and management of acne. • First nationwide study uses big data and machine learning to predict skin illness • Built environments significantly contribute to acne prevalence and severity • Lifestyle features have the highest collective contribution to acne severity • Non-linear models better explain the link between built environments and acne This study underscores the significant role of the urban built environment in influencing both the risk and severity of chronic acne, revealing non-linear effects of environmental factors on acne development. The findings provide a scientific basis for incorporating acne into the framework of medical geography research on the impact of the built environment. Moreover, this study offers new insights into the prevention and management of acne, suggesting that future research and clinical practice should consider the role of built environment factors and their interactions with individual behaviors and treatment approaches. Understanding external exposome factors is key to advancing acne prevention and treatment. While built environments (BEs) are linked to various chronic conditions, their impact on acne has been unclear. Using machine learning-based analyzing data from 23,488 undergraduates across 90 campuses in China, Yang et al. identify significant contribution of BEs to both the prevalence and severity of acne. Curvilinear relationships between acne and most BEs highlight the need to target specific environmental factors for effective acne management strategies.